{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from torch.nn import functional as F\n",
    "\n",
    "from dataset import *\n",
    "from module import *\n",
    "%matplotlib inline\n",
    "torch.cuda.is_available() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define some constants\n",
    "TOKENIZE_MAX_LENGTH = 80\n",
    "WORD_VEC_DIM = 256\n",
    "\n",
    "# init dataset and other stuff\n",
    "torch.manual_seed(42)\n",
    "root_dir = \"../deepfashion-multimodal/\"\n",
    "image_dir = path.join(root_dir, \"images\")\n",
    "train_dataset = DeepFashionDataset(path.join(root_dir, \"train_captions.json\"), True)\n",
    "test_dataset = DeepFashionDataset(path.join(root_dir, \"test_captions.json\"), False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init resnet, transformer etc.\n",
    "def init_train(_device=torch.device(\"cpu\"), **kwargs):\n",
    "    _resnet_transformer = MyTransformer(**kwargs).to(_device)\n",
    "    return _resnet_transformer, None, _device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{',': 6,\n",
      " '.': 64,\n",
      " '<end>': 112,\n",
      " '<pad>': 110,\n",
      " '<start>': 111,\n",
      " '<unk>': 113,\n",
      " 'Her': 41,\n",
      " 'His': 60,\n",
      " 'It': 44,\n",
      " 'T-shirt': 70,\n",
      " 'The': 78,\n",
      " 'There': 45,\n",
      " 'This': 1,\n",
      " 'a': 95,\n",
      " 'accessory': 89,\n",
      " 'also': 40,\n",
      " 'an': 84,\n",
      " 'and': 77,\n",
      " 'are': 51,\n",
      " 'belt': 102,\n",
      " 'block': 63,\n",
      " 'chiffon': 50,\n",
      " 'clothes': 13,\n",
      " 'clothing': 10,\n",
      " 'color': 0,\n",
      " 'complicated': 65,\n",
      " 'cotton': 46,\n",
      " 'crew': 36,\n",
      " 'cut': 24,\n",
      " 'denim': 101,\n",
      " 'fabric': 100,\n",
      " 'female': 103,\n",
      " 'finger': 107,\n",
      " 'floral': 38,\n",
      " 'furry': 99,\n",
      " 'gentleman': 4,\n",
      " 'glasses': 66,\n",
      " 'graphic': 53,\n",
      " 'guy': 28,\n",
      " 'hands': 20,\n",
      " 'has': 16,\n",
      " 'hat': 61,\n",
      " 'head': 5,\n",
      " 'her': 73,\n",
      " 'his': 12,\n",
      " 'in': 49,\n",
      " 'is': 52,\n",
      " 'it': 18,\n",
      " 'its': 72,\n",
      " 'knitting': 15,\n",
      " 'lady': 11,\n",
      " 'lapel': 62,\n",
      " 'lattice': 106,\n",
      " 'leather': 26,\n",
      " 'leggings': 29,\n",
      " 'length': 8,\n",
      " 'long': 47,\n",
      " 'long-sleeve': 79,\n",
      " 'lower': 21,\n",
      " 'man': 3,\n",
      " 'medium': 2,\n",
      " 'medium-sleeve': 88,\n",
      " 'mixed': 81,\n",
      " 'neck': 33,\n",
      " 'neckline': 54,\n",
      " 'neckwear': 35,\n",
      " 'no': 71,\n",
      " 'of': 76,\n",
      " 'off': 27,\n",
      " 'on': 108,\n",
      " 'or': 68,\n",
      " 'other': 58,\n",
      " 'outer': 75,\n",
      " 'pair': 57,\n",
      " 'pants': 69,\n",
      " 'pattern': 22,\n",
      " 'patterns': 97,\n",
      " 'person': 59,\n",
      " 'plaid': 74,\n",
      " 'pure': 67,\n",
      " 'ring': 42,\n",
      " 'round': 48,\n",
      " 'shirt': 83,\n",
      " 'shoes': 90,\n",
      " 'short': 93,\n",
      " 'short-sleeve': 37,\n",
      " 'shorts': 43,\n",
      " 'skirt': 91,\n",
      " 'sleeveless': 86,\n",
      " 'sleeves': 92,\n",
      " 'socks': 104,\n",
      " 'solid': 25,\n",
      " 'square': 98,\n",
      " 'stand': 34,\n",
      " 'stripe': 96,\n",
      " 'striped': 39,\n",
      " 'sunglasses': 32,\n",
      " 'suspenders': 85,\n",
      " 'sweater': 55,\n",
      " 'tank': 80,\n",
      " 'the': 56,\n",
      " 'this': 82,\n",
      " 'three-point': 17,\n",
      " 'three-quarter': 31,\n",
      " 'top': 109,\n",
      " 'trousers': 7,\n",
      " 'upper': 87,\n",
      " 'v-shape': 19,\n",
      " 'waist': 94,\n",
      " 'wearing': 9,\n",
      " 'wears': 14,\n",
      " 'with': 105,\n",
      " 'woman': 30,\n",
      " 'wrist': 23}\n"
     ]
    }
   ],
   "source": [
    "# calculate some indicators about the dataset labels\n",
    "import pprint\n",
    "\n",
    "\n",
    "def load_word_map(_dir=\"./word_map.json\"):\n",
    "    if os.path.exists(_dir):\n",
    "        with open(_dir, \"r\") as f:\n",
    "            _word2id = json.load(f)\n",
    "        _id2word = {v: k for k, v in _word2id.items()}\n",
    "        return _word2id, _id2word\n",
    "\n",
    "word2id, id2word = load_word_map(\"./word_map.json\") # type: ignore\n",
    "word_num = len(word2id)\n",
    "pprint.pprint(word2id)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "\n",
    "# BATCH_SIZE = 128\n",
    "BATCH_SIZE = 32\n",
    "learning_rate = 3e-4\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "resnet_transformer, optimizer, device = init_train(\n",
    "    word_num=word_num,\n",
    "    word_vec_dim=WORD_VEC_DIM,\n",
    "    _device=device,\n",
    "    num_decoder_layers=6,\n",
    "    num_encoder_layers=6,\n",
    "    activation=\"gelu\",\n",
    "    dropout=0.1,\n",
    "    temperature=10000,\n",
    "    nhead=8,\n",
    "    dim_feedforward=2048,\n",
    "    custom_encoder=None,\n",
    "    custom_decoder=None,\n",
    "    layer_norm_eps=1e-5,\n",
    ")\n",
    "model = \"./model/8head/model_epoch30_loss1.56678.pth\"\n",
    "\n",
    "# model = \"./model/16head/model_epoch40_loss1.9063.pth\"\n",
    "resnet_transformer.load_state_dict(torch.load(model)[\"model\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "\n",
    "def bleu_score(hypo, reference):\n",
    "    hypo = hypo.split()\n",
    "    reference = [reference.split()]\n",
    "    return sentence_bleu(reference, hypo)\n",
    "\n",
    "\n",
    "def generate_caption(_model: MyTransformer, image: torch.Tensor, max_length=50):\n",
    "    \"\"\"\n",
    "    根据输入的图像生成字幕。\n",
    "\n",
    "    参数:\n",
    "        model (MyTransformer): 训练好的模型。\n",
    "        image (torch.Tensor): 输入图像，应该与训练时的图像格式相同。\n",
    "        max_length (int): 生成字幕的最大长度。\n",
    "\n",
    "    返回:\n",
    "        str: 生成的字幕。\n",
    "    \"\"\"\n",
    "    _model.eval()  # 将模型设置为评估模式\n",
    "    with torch.no_grad():  # 关闭梯度计算\n",
    "        # 假设image已经是预处理过的\n",
    "        image = image.unsqueeze(0)\n",
    "        for _ in range(3):\n",
    "            input_seq = torch.tensor([[word2id[\"<start>\"]]]).to(image.device)  # \"<start>\"标记序列开始\n",
    "            for _ in range(max_length):\n",
    "                output = _model.forward(image, input_seq)  # 前向传播\n",
    "                prob = F.softmax(output[-1], dim=-1)  # 计算概率\n",
    "                sample = torch.multinomial(prob, 1)[-1].unsqueeze(0)  # 从概率分布中采样\n",
    "                # sample = torch.argmax(output, 2)\n",
    "                next_word = sample[-1, 0].item()\n",
    "                input_seq = torch.cat([input_seq, sample], dim=1)  # 更新输入序列\n",
    "\n",
    "                if next_word == word2id[\"<end>\"]:  # \"<end>\"标记序列结束\n",
    "                    break\n",
    "                # print(id2word[next_word]) # type: ignore\n",
    "            if len(input_seq[0]) <= 5:\n",
    "                continue\n",
    "        # 将索引转换为单词，并去除起始和结束标记\n",
    "        caption = [id2word[idx.item()] for idx in input_seq[0, 1:-1]]  # type: ignore\n",
    "    return \" \".join(caption), len(caption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(142)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating:   0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating: 100%|██████████| 1/1 [00:02<00:00,  2.14s/it]\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================\n",
      "Genrate: The T-shirt this woman wears has short sleeves , its fabric is denim , and it has solid color patterns . The T-shirt has a round neckline . This woman wears a three-point shorts , with denim fabric and solid color patterns . The lady wears a ring . There is an accessory on her wrist . The female wears sunglasses . This lady is wearing sunglasses .\n",
      "Reference: The lady is wearing a short-sleeve T-shirt with solid color patterns and a three-point pants. The T-shirt is with cotton fabric and its neckline is square. The pants are with denim fabric and pure color patterns. This lady wears a ring. This lady wears sunglasses. There is an accessory on her wrist.\n",
      "Meteor Score: 0.5451331306430808\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from meteor_util import calculate_meteor_score\n",
    "rand_idx = np.random.randint(0, len(test_dataset))\n",
    "test_img, reference = test_dataset[rand_idx]\n",
    "\n",
    "test_img = test_img.to(device)\n",
    "\n",
    "sens = []\n",
    "for i in tqdm(range(1),desc=\"Generating\"):\n",
    "    sentence, _len = generate_caption(resnet_transformer, test_img, max_length=80)\n",
    "    score = calculate_meteor_score(sentence,reference )\n",
    "\n",
    "plt.imshow(test_img.cpu().permute(1, 2, 0))\n",
    "print(\"=\"*20)\n",
    "print(\"Genrate:\", sentence)\n",
    "print(\"Reference:\", reference)\n",
    "print(\"Meteor Score:\", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
